IMPLEMENTASI ALGORITMA DECISION TREE PADA APLIKASI FITTING SEPEDA ROAD BIKE
DOI:
https://doi.org/10.71282/jurmie.v3i6.2251Keywords:
Bike Fitting, Decision Tree, Machine Learning, Flask, Road Bike.Abstract
Road bikes are becoming increasingly popular in Indonesia; however, many cyclists experience discomfort due to mismatched bicycle component sizes with their body dimensions. Professional bike fitting services are available but require relatively high costs and are not easily accessible to all demographics. This study aims to develop a website-based road bike fitting application called SepedaKu that utilizes the Decision Tree algorithm to provide automatic bicycle component size recommendations. The application is built using the Flask (Python) framework as the backend and HTML, CSS, JavaScript for the frontend. The Decision Tree Classifier algorithm is used to predict frame, stem, and handlebar sizes, while the Decision Tree Regressor is used to predict saddle height. Testing was conducted using the Black Box Testing method and user satisfaction surveys with a Likert scale involving 10 respondents. The test results show that all application features function properly, the prediction model achieves 80% accuracy for classification and a Mean Absolute Error (MAE) of 1.20 cm for regression, and the user satisfaction level reaches 85%, which falls into the excellent category.
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Copyright (c) 2026 Ahmad Fadhilah, Isram Rasal (Author)
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